Python supports four main data structures (list, tuple, set, and dictionary) and there are a number of important built-in methods around these data structures.

Lists are used to group together values of single or multiple data types. The list structure can be assigned by stating values in square brackets with a comma (,) as a separator:

>>> myList = ['a', 2, 'b', 12.0, 5, 2]
>>> myList
['a', 2, 'b', 12.0, 5, 2]

Like strings, values in a list can be accessed using index numbers, which starts from 0. A feature called slicing is used by Python to obtain a specific subset or element of the data structure using the colon operator. In a standard format, slicing can be specified using the myList[start:end:increment] notation. Here are a few examples to better understand the notion of slicing:

Tuples are immutable data structures supported by Python (different from the mutable structures of lists). An immutable data structure means that you cannot add or remove elements from the tuple data structure. Due to their immutable properties, tuples are faster to access compared to lists and are mostly used to store a constant set of values that never change.

The tuple data structure is declared like list, but by using parentheses or without any brackets:

>>> tupleA = 1, 2, 3
>>> tupleA
(1, 2, 3)
>>> tupleB = (1, 'a', 3)
>>> tupleB
(1, 'a', 3)

Just like in a list data structure, values in tuple can be accessed using index numbers:

>>> tupleB[1]
'a'

As tuples are immutable, list manipulation methods such as append(), insert(), and pop() don't apply for tuples.

The set data structure in Python is implemented to support mathematical set operations. The set data structure includes an unordered collection of elements without duplicates. With its mathematical use cases, this data structure is mostly used to find duplicates in lists, as conversion of a list to a set using the set() function removes duplicates from the list:

>>> listA = [1, 2, 3, 1, 5, 2]
>>> setA = set(listA)
>>> setA
set([1, 2, 3, 5])

The dict data structure is used to store key-value pairs indexed by keys, which are also known in other languages as associative arrays, hashes, or hashmaps. Unlike other data structures, dict values can be extracted using associated keys:

>>> boards = {'uno':328,'mega':2560,'lily':'128'}
>>> boards['lily']
'128'
>>> boards.keys()
['lily', 'mega', 'uno']

The if statement is the most basic and standard statement used to set up conditional flow. To better understand the if statement, execute the following code in the Python interpreter with different values of the age variable:

>>> age = 14
>>> if age < 18 and age > 12:
 print "Teen"
elif age < 13:
 print "Child"
else:
 print "Adult"

This will result in Teen being printed on the interpreter.

Python's for statement iterates over the elements of any sequence according to the order of the elements in that sequence:

>>> celsius = [13, 21, 23, 8]
>>> for c in celsius:
 print " Fahrenheit: "+ str((c * 1.8) + 32)

This will result in the Python interpreter generating the following output that will display the calculated Fahrenheit values from the given Celsius values:

Fahrenheit: 55.4
Fahrenheit: 69.8
Fahrenheit: 73.4
Fahrenheit: 46.4

The while statement is used to create a continuous loop in a Python program. A while loop keeps iterating over the code block until the condition is proved true:

>>> count = 5
>>> while (count > 0):
 print count
 count = count - 1

The while statement will keep iterating and printing the value of the variable count and also reduce its value by 1 until the condition, that is (count > 0), becomes true. As soon as the value of count is lower than or equal to 0, the while loop will exit the code block and stop iterating.

The other compound statements supported by Python are try/catch and with. These statements will be explained in detail in the upcoming chapters. Python also provides loop control statements such as break, continue, and pass that can be used while a loop is being executed using the compound statements mentioned earlier. You can learn more about these Python features from https://docs.python.org/2/tutorial/controlflow.html.

Conversion methods such as int(), float(), and str() can convert other data types into integer, float, or string data types respectively:

>>> a = 'a'
>>> int(a,base=16)
10
>>> i = 1
>>> str(i)
'1'

Similarly, list(), set(), and tuple() can be used to convert one data structure into another.

>>> list = [1.12, 2, 2.34, 4.78]
>>> min(list)
1.12
>>> max(list)
4.78

>>> pow(3.14159, 2)
9.869587728099999